User Authentication Based on Keystroke Dynamics

This paper presents a technique to verify user identity using keystroke dynamics from short text, namely the computer login string. The keystroke behavioural pattern is obtained when a person types with a QWERTY keyboard. Two features hold time of an individual key and the latency of the consecutive keystrokes is used for authentication. Using a small training sample, accuracies of 90% and 99% are achieved for the data-set of 220 login strings per user (40 strings from legal user +180 strings from nine intruders) using Gaussian mixture model and two-layer feed-forward neural network, respectively, as classifier. The paper then proceeds to a comprehensive study to explain how the accuracy varies with the length of the input string and with negative data in the training set.